Gin Config PyTorch Pipeline Tutorial

A tutorial implements a Gin Config controlled PyTorch pipeline where training code stays stable while experimental parameters move to declarative config files. The pipeline constructs a nonlinear spiral binary classification task with a configurable MLP featuring scoped architectural variants. Parameters for optimizer, scheduler, loss, batching, seeding, and training loop are exposed via @gin.configurable bindings with runtime overrides.